Precision Agriculture, past, present and future_Dennis Francis_Siagro2014_Embrapa Instrumentação

1. Precision Agriculture
Past - - -
- - - Present - - -
- - - Future
Dr. Jim Schepers or
Dr. Dennis Francis
USDA-Agricultural Research Service
( both happily retired )

2. Thanks - - for the invitation to the 2014
Precision Agriculture National Symposium
My first trip to Brazil (Sete Lagoas) was in January 1995

3. Nebraska
Corn Soybean
Wheat

4. Farmers are great observers !
BUT
May not be able to explain
their observations
AS SUCH
Producers welcome scientific information and are eager
to learn from the discussion

5. Initial precision agricultural efforts were driven by industry
Outcome: Spatial variability in crop vigor and yield was only partially removed
Need: Technologies to quantify yield variability
• Variable-rate multi-nutrient applications were based on soil testing data
• GPS was not available
Six nutrients

6. Soil Survey
Bare Soil Image
Grid Soil Sampling
(34 samples / ha)
Computer Generated Management Zones

7. EM-38 (electro-magnetic induction)
Proxy for soil electrical conductivity

8. Veris (electro-magnetic induction)

9. Soil Survey Yield Map
Electrical
Conductivity
2000

10. Computer Generated Management Zones
Incorporating. . .
Bare Soil Image
Elevation
Slope
EM-38

11. First yield-monitor combines were tested in 1992
Situation: Required three computers
• Grain flow
• Grain moisture
• GPS (initially scrambled)
• Yield-monitors are common on new combines
• Grain protein content monitors are now available
• Yield maps are not fully utilized

12. Crop canopy sensor research was initiated in 1993
Situation: Chlorophyll meters worked well for
research purposes, but are not practical for
commercial fields
Therefore: Need for mobile devices to provide
information related to crop biomass (size of the
factory) and canopy chlorophyll content
(photosynthesis)
First crop canopy sensors used natural lighting (known as passive sensors)
Active sensor research initiated in 1999
Introduced in 1990
Problems: clouds
shadows
changes in brightness during the day
Attributes: generated modulated light
no affect of shadows
operational any time of the day
can be used to facilitate “on-the-go” nutrient applications

13. Most individuals agree that - - “seeing is believing”,
AND
the human eye is especially sensitive to green colors
Ultraviolet (UV) Near Infrared (NIR)
BUT
Humans cannot see near infrared light, which is reflected by
living vegetation (also called biomass)
AS SUCH
Farmers largely base their assessment of crop vigor on
“greenness” (related to chlorophyll content and nitrogen status)

14. Remember - - - -
Canopy sensors respond to “living biomass”
and “chlorophyll content”
Treatments / N-rates
N Status
Similar
Canopy sensors can not quantify excess N
AND
Soil background reduces sensitivity

15. Sensors can be designed to be sensitive to Greenness -
(i.e., chlorophyll and nitrogen status in most cases)
AND TO
near infrared (NIR) light - (“living vegetation” called biomass)
BASICALLY
NIR - size of the factory (cumulative indicator of canopy size )
(number and size of leaves)
Chlorophyll - output potential of leaves via photosynthesis
RESULTING IN
Yield and potential profitability
- - - - This why we need to measure chlorophyll and biomass

16. Crop Circle ACS-430
or
AgLeader OptRx

17. Mexico - White Corn, 2010

18. Irrigated Corn - Nebraska
Drop
Nozzles

19. Variable-Rate N Injection

20. Precision Agriculture (Site-Specific Management) is
not a “one-size-fits-all” proposition or universal
solution to address spatial variability
• Different field sizes
• Different crops
• Different soils
• Uncertainty about climate and water availability
• Spatial variability in nutrient status
• Availability of field equipment is important
• Implementation might require technical assistance
High yields may not be highly correlated with profitability
Sustainability requires a multi-year analysis

21. Perceptions - may not be true, but they are “REAL”
in the minds of individuals
• I’m too old to learn
• Too expensive
• My fields are uniform
• I’m still farming, so my operation must be sustainable
• Too much risk
• I already demonstrate environmental stewardship
• Technical assistance is not available or is unreliable

22. Original 4-Rs
Applied at the field scale - - delineates management zones
RIGHT - Place
Rate
Time
Form
How to create a better environment for plants ?
Consider: compaction
nutrient placement
plant competition
weeds

23. Proactive - Plan ahead and lock-in decisions
based on: soil texture and water holding capacity
nutrient information
anticipated weather
yield goal
Reactive - Monitor weather and crop vigor to make in-season
adjustments (also called “adaptive management”)
according to: available soil water
anticipated weather
estimated nutrient losses thus far in the season
crop vigor (sensors or remote sensing)
changes in yield potential

24. Potentially
Useful
Technologies
United
States Brazil
Variable-rate planting moderate _____
Multiple-cultivar planting low _____
Variable-rate lime high _____
Variable-rate nutrients moderate _____
Yield mapping high _____
Crop canopy sensors moderate _____
Remote sensing moderate _____
Climate & growth models ??? _____
High-clearance applicators moderate _____
Crop consultants moderate ++ _____
Auto-guidance high _____

25. Harnessing Precision Agriculture information is like
making sausage !
Needs multiple ingredients in the right proportions

26. Precision Agriculture is about innovation and
thinking outside the box
How would you connect these nine points with four continuous lines ?
1
2
3
4
Think outside the box !

27. Soil
Texture
Landscape
Position
Slope
Soil
Color
Elevation
Productivity

28. So Much for the Concept of Precision Agriculture
How did the Embrapa / USDA-ARS partnership come to be ?
The relationship started in January 1995 - A friend, Walter
Baethgen, from Uruguay put me in contact with Goncalo
Franca with Embrapa at Sete Lagoas.
A graduate student, scientist with Li-Cor, and Jim Schepers tested prototype passive
sensors with the assistance of Goncalo Franca and Evandro Mantovani (also hosted
by Bob Schaffert, Antonio Bahia, and Mauricio Lopes).
Morethson Resende (water management) and Derli Santana (soil classification)
traveled to Nebraska while on sabbatical leave (1995-1997).
A team of nine USDA-ARS scientists visited five Embrapa locations in 1996. Precision
agriculture was identified as a discipline of interest for scientific exchanges. This was
the beginning of the LABEX program.

29. LABEX Friends who spent time in Nebraska
Ariovaldo
Luchiari
Moresthon
Resende
Ricardo
Inamasu
Evandro
Mantovani
Others without Pictures
Scientists Graduate Students
Goncalo Franca Antonio Coehlo
Derli Santana Jaoa Camargo Neto
Frederico Duraes Luciano Shiratsuchi
Jose Molin Lucas Amaral

30. Field Researchers - 2000

31. Ricardo Inamasu
Examples of imagination, innovation, determination and execution

32. “It’ cold - like hell - in Nebraska”
Jim Schepers
402-310-6150
james.schepers@gmail.com

33. Optimizing In-season Nitrogen
Management
Dennis D. Francis and James S. Schepers

34. Early
Growth
Rapid
Growth Maturing
Late
Loss
100
75
50
25
0
40% of requirement after silking
May June July Aug Sept
Seasonal Nitrogen Uptake, %
70-80% of requirement after V8 - 10

35. Early N
Planting
In-season N
V6 V9 Tasseling
When?
How much?
How much early?
In season?
Sensor based
N uptake

36. Photosynthesis
Biomass
Near Infrared
Wavebands
(NIR)
Sensors Measure
● Disappearance of red light
● Abundance of reflected NIR
Chlorophyll
Chlorophyll captures BLUE and RED light
Reflects NIR
Visible
Wavebands

37. Normalize Vegetation Index Values to
remove “field effects”
Compare all data to “Healthy Plants” that have the same:
Growth stage
Cultivar (variety)
Previous crop
Water management
Soil properties - - - except nutrients
If one assumes all nutrients are adequate except for N, for example :
----------------------------
- Differences in crop vigor are probably related to plant N status -

38. Common Vegetation Indices
NDVI =
NDRE =
Chl Index =
Visible / NIR =
(NIR – Red)
----------------------------------
(NIR + Red)
(NIR – Red Edge)
--------------------------------------------------
(NIR + Red Edge)
(NIR – Red)
-----------------------------------------------
(Red Edge – Red)
(Red)
----------------
(NIR)
(NIR)
OR - 1
------------------------------
(Red Edge)

39. Sensors only Measure Bulk Reflectance
N
P
Fe
- Many factors can influence leaf chlorophyll content -

40. Algorithm Comparison
“Why not compare nitrogen recommendations
using a common data set ?”
Algorithms have been developed using specific sensors
that are associated with recommended agronomic
practices
• Wave-band differences
• Reference Strategy (normalization)
• Opportunity for producer input
• Preplant N differences
• Yield (relative, predicted, not used)
• NUE input
patents

41. Topcon
Holland Scientific NTech Industries
(Ag Leader) (Trimble)

42. Topcon
Crop Spec
Foot-print
changes
with plant
height

43. Modulation/Demodulation Using
Polychromatic LEDs
LED
TARGET
PD1
User Selected
SENSOR
PD2
Filters
ACS-470
PD3

44. Relative “Vigor”
(i.e., 92% adequate)
“ Target “
Reference “Happy Crop”
“Managed Crop” = = S I
• N-rich (highest 3 consecutive seconds) GreenSeeker
• N-rich (average) Missouri
• Virtual reference from field with modest preplant N Holland

45. Reference Value Determination
Not used “Walk in the field”
“…we filter off values from an N-rich area that would represent poor
or no stand and then take the mean of what remained”. (Newell
Kitchen)
German
Missouri
Oklahoma
Holland
“Reference values are tied to the readings we collect from the N-rich
strip (producers and research). “ (Bill Raun)
Earlier used highest 3-consecutive second value (30-point running average)
“Virtual reference” - 95-percentile value (2 Std. Dev. units above
the mean) for histogram of vegetation index values from field strip
receiving modest preplant N application. (~1/3 recommended rate)
“Drive-and-Apply” - Automatically updated virtual reference when starting
in average to above-average part of the field.

47. N Credits
Preplant N
EONR
Producer Optimum
N Accumulation
(based on growth stage)
Sufficiency Index
Back-Off Strategy
SI to start cutback
SI to cut-off
Algorithm
Spatial
Soil / Topography
Adjustment
14
12
10
8
6
4
2
0
0 50 100 150 200 250 300
Fertilizer N Rate, kg/ha
Yield, Mg/ha
Field
Reference

48. German Algorithm
• Producer identifies poor part of the field and assigns the
desired N rate.
• Similarly, the producer identifies a good part of the field and
assigns the preferred N rate.
• Sensor readings are taken from each area.
• Linear regression algorithm is developed for variable rate
application.
Developed for small grains with multiple applications.

49. Oklahoma Algorithm
Assess crop growth and vigor via sensor readings:
• Record growing degree days (GDD) since planting
• Calculate potential yield based on NDVI / GDD data vs. yields
from past years and locations
• Back calculate the difference in grain N content between
reference and target plants
• Adjust for NUE
New Approach
• Use reference NDVI and yield data from multiple locations and
years to generate sigmoid-shaped growth function
• Predict yield potential from NDVI using sigmoid function

50. Missouri Algorithm
Crop Sensor Stage Missouri N rate equation
Corn GreenSeeker V6-7 (12" to knee high) (220 x ratio*) - 170
Corn GreenSeeker V8-10 (waist high) (170 x ratio) - 120
Corn GreenSeeker V11+ (chest high or taller) (160 x ratio) - 130
Corn Crop Circle 210 V6-7 (330 x ratio) - 270
Corn Crop Circle 210 V8-10 (250 x ratio) - 200
Corn Crop Circle 210 V11+ (240 x ratio) - 210
--------------------
*Ratio = (Target visible/near-infrared) / (High-N visible/near-infrared)
• Calibrated using field studies
• Minimum N-rate of 30 – 60 lb/acre regardless of sensor reading
Source: “Managing Nitrogen with Crop Sensors: WHY and HOW” (Scharf)
“ SI “

51. Holland / Schepers Algorithm
Based on the mathematical derivation from an
N-rate vs. yield response function (quadratic
component)
--------------------
ΔSI √ Farmer’s optimum rate
N Rec = (Nopt – Ncredits) x (1 - SI)
or EONR
Source: “Derivation of a Variable Rate Nitrogen Application Model for In-Season
Fertilization of Corn” (Holland and Schepers, Agron. J. 102:1415–1424 (2010)

52. *Holland-Schepers Generalized N-Rate Model:
1 
APP i OPT CRD N - N
S
N MZ
I
S
I
  
 





 
Management Inputs and Credits Sensor Data
Zone Scaling
*Reference
Holland, K. H., & Schepers, J. S. (2010). Derivation of a variable rate nitrogen application model for in-season fertilization of corn.
Agronomy Journal 102:1415-1424.

53. Other Features
German
Missouri
Oklahoma
Holland
N Cut-Back Yield Management Genetic
Credits Feature Prediction Zones Potential
No No No No No
No No No No No
No Yes / No Yes No No
Yes Yes No Yes Yes